Partial Differential Equations:
An Overview
Lecture 1
1 Model Equation
1.1 Convection-Diffusion
Despite its apparent simplicity, this equation appears in a wide range of disci plines ranging from Heat Transfer to Financial Engineering. During this course
we will make extensive use of this equation, and several of the limiting cases
contained therein, to illustrate the numerical techniques that will be presented.
In some cases U, k, and f will be functions of the solution u, in which case the
equation is said to be nonlinear.
Note 1 D e r i v a t i o n o f t h e C o n v e c t i o n - D i f f u s i o n E q u a t i o n f o r H e a t
Transfer
We sketch below the derivation of the Convection-Diffusion equation for the
particular problem of Heat Transfer in a moving fluid. Consider a velocity field
U = (U(x,y), V(x,y)) which is (for simplicity) time independent and incom pressible, e.g.
A streamline is a curve witch is obtained by integrating the vector field and
corresponds to the trajectory of a fluid parcel
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For a given parcel we can write the equation expressing the balance of energy
as
where E=ρcT is the internal energy per unit volume, T is the temperature, ρ
is the density and c is the specific heat. The term Qc can be expressed in terms
of the heat flux q = (qx,qy), by considering an infinitesimal parcel of size dx dy.
The net rate of the heat transferred into the parcel, per unit area, will thus be
The heat flux is finally related to the temperature through Fourier’s law
where k is the (constant) thermal conductivity of the fluid.
The derivative term
is called a material derivative because it is associated
with a fixed fluid parcel that moves with the flow.
Expressing the material derivative (Lagrangian notion) in terms of fixed time
and space derivatives (Eulerian notion) we obtain
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which then yields the final form of the convection-diffusion equation after dividing by
and defining
Note 2
Classification of PDE's (Optional Reading)
We review the classification of first and second order linear partial differential
equations in two independent variables. This classification is useful, not only
to identify solution methods applicable to a particular equation type, but also
to determine the character of the solutions. For two independent varia bles all
equations can be classified as shown below. We note that for equations with
more than two independent variables, the classification is far more complex as
some equations may become of mixed type (see [F] for additional reading)
Within this note, (x,y), denotes the independent variables and Φ(x,y), the
dependent variable, or solution of the PDE.
First Order PDE's
First order partial differential equations are always of hyperbolic type. A
general linear first order equation can be written as
where A and B may be functions of x and y, but not of Φ. If we write dΦ =
Φxdx + Φydy, then
Along the lines (characteristics) such that Bdx - Ady = 0,
Characteristics are Bx - Ay = Ψ, for any Ψ, and the general solution becomes
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Where q is an arbitrary function to be determined by the initial and boundary
conditions.
Second Order PDE's
A linear second order partial differential equation can be written as
where A, B and C may be functions of x and y. Based on the local value of the
coefficients the equations are classified as follows:
B2 - 4AC > 0
B2 - 4AC = 0
B2 - 4AC < 0
Hyperbolic
Parabolic
Elliptic
Note that an equation may change type from one point to ano ther since the
coefficients may be functions of x and y. We will typically assume that, when
we say that an equation is of a given type, it remains of the same type over the
whole domain.
Consider a valid change of independent variables
, such
that
Then,
The transformed equation becomes
with
THEOREM : This classification is invariant under valid non-singular transformations.
Proof: From above
CANONICAL FORMS
HYPERBOLIC case (B2 - 4AC > 0):
In this case it is always possible to choose
so that a = c = 0, i.e.
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Then, the equation becomes
An alternative form can be obtained by setting
PARABOLIC case (B2 - 4AC = 0) :
Here, we can only set a (or c) to zero (not both), otherwise
independent. If we set a = 0, then
, and
are not
It can be verified, by direct evaluation, that in this case b = 0, in which case we
can pick
to be any function such that |J| ≠ 0, and the equation becomes:
ELLIPTIC case (B2 - 4AC < 0) :
This case is identical to the hyperbolic case but now
conjugates (B 2 - 4AC < 0). Take
becomes:
1.1.l
Applications
If u is
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and
are complex
and the equation

Temperature  Heat Transfer

Pollutant Concentration  Coastal Engineering

Probability Distribution  Statistical Mechanics
This equation is known in Statistical Mechanics as the Fokker-Planck
equation.

Price of an Option  Financial Engineering
This equation is known in Financial Engineering as the Black-Scholes
Equation.

...
In some of the above cases the equation is slightly different (e.g. particular
non-constant coefficients), however the basic form remains invariant.
2 Limiting Cases
2.1 Elliptic Equations

“Smooth" solutions
-- even when the boundary conditions or f are not smooth

The domain of dependence of u(x, y) is Ω
This means that a small perturbation of f, or boundary conditions, anywhere in
the domain will alter the value of u(x,y).
The solution of elliptic equations will be studied extensively in this course. For
these equations we will be presenting solution techniques using Finite Differ ences, Finite Elements and Boundary Integral Methods.
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2.2 Parabolic Equations

“Smooth" solutions
-- even when the initial, boundary conditions, or f, are not smooth.

The domain of dependence of u(x, y, T) is (x, y, t <T)
In this course we will address the solution of parabolic equations using Finite
Difference and Finite Element Methods.
2.3 Hyperbolic Equations

Non-smooth solutions

Characteristics :

The domain of dependence of u(x,T) is (xc(t), t<T)

Non-smooth solutions

Characteristics are streamlines of U, e.g.

The domain of dependence of u(x) is (xc(s), s < 0)
We will present Finite Difference and Finite Volume Methods for solving hyperbolic equations. In particular, Finite Volume Methods will be extended to deal
with non-linear hyperbolic equations.
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2.4 Eigenvalue Problem
We shall see below that the eigenvalue problem of a given spatial operator is
closely related to the temporal evolution of the solution of the associated time dependent equation. The particular eigenvalue problem shown here is closely
related to the Heat Equation.
2.5 One Spatial Variable
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Fourier Analysis
3.1 Definition
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The following orthogonality relationship satisfied by the Fourier modes can be
easily verified
We recall that
is the Kronocker symbol and is equal to 1 if k = k’ and
0 otherwise. Using the above relationship, the coefficients
can be computed
directly as
Note that when
is real,
where * denotes complex conjugate.
Note 3
Fourier series
Fourier series are only denned for functions which satisfy the following regularity
condition
We also note that the decomposition presented above can be written in an
equivalent form as
with
and
.
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3.2 Example
The above figures show two functions together with a plot of the amplitude of
their Fourier coefficients. An important feature of the Fourier coefficients is
that they are directly associated with the smoothness of the original function:
we can intuitively see that the less oscillatory the function, the faster |gk | will
tend to zero as |k| becomes large, since we will need less "high frequency" (small
wavelength) contributions.
3.3 Differentiation
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Since differentiation decreases the rate of decrease of the Fourier coefficients, we
must assume that our function is sufficiently smooth (|Uk|  0 fast enough)
in order to meaningfully perform this operation. In particular, it can be shown
that, if a function has p bounded derivatives, |u||k| p+1 < ∞ as |k|  ∞ .
3.4 Poisson Equation
The number and type of boundary conditions required for this equation will be
discussed further during the course; however it should be clear that, in one
dimension, a linear equation involving an n-th order spatial operator will require
n boundary conditions.
Note 4
Existence and Uniqueness of the Solution
Physically, there can be no net generation since the flux in (ux (0)) equals the
flux out (ux(2π)). Also note that, from the equation u is free to "float" and the
condition
pins the solution. Physically, the level of u is determined
by initial conditions for the heat equation of which -uxx = f is the long-time
limit.
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==>
- the solution u is smoother than f
3.4.1 Example
3.5 Heat Equation
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==>
- exponential decay of initial condition (dissipation)
- higher decay for "higher modes" (larger k) ≡ smoothness
3.5.1 Example
3.6 Wave Equation
13
==>
- no decay, propagation with wave speed c = U
- no dispersion (c constant) ≡ invariant shape
3.6.1 Example
3.7 General Operator
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Note 5
Disispersion
Whenever the speed of propagation depends on the wavelength 2π/k (or wavenumber k}, we say that the wave is dispersive. A consequence of dispersion is that
the shape of the solution will change as the wave propagates. We see from the
above table that for n = 1 (the wave equation), the speed of propagation is
constant for all modes and therefore any initial shape will be preserved as it
propagates. For n = 3, c = -k2 and waves with high |k| will propagate a lot
faster than the waves with small |k|, and therefore any initial condition will
change its shape as the solution evolves.
We also note that when different derivative terms are present, the solution will
exhibit a behaviour which is the result of combining the different terms present.
For instance an equation such as ut = -ux + u xx will exhibit both propagation
and decay; this can easily be seen by noting that in this case σ = -ik - k2.
3.8 Eigenvalue Problem
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The eigenvectors associated with λn are:
We note that the two eigenvectors corresponding to the eigenvalue An can be
combined into sin nx and cos nx. We note also that in addition to the eigenvalues
and eigenvectors shown above, A0 = 0, u0(x) = 1 is also an eigenvector.
Since the eigenmodes coincide with the Fourier modes, we can regard our original
Fourier expansion as an expansion in terms of eigenmodes. In fact, this latter
interpretion is quite general and applies in situations in which the Fourier modes
either do not exist, or do not coincide with the eigenmodes of the differential
operator.
4 Eigenvalue Expansions
4.1 Formal Extension
Here we are assuming that the eigenvalues/eigenvectors have be en ordered in
such a way that if an eigenvalue has a multiplicity m it will contribute m terms
to the sumation.
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Note 6
Separation of Variables
We observe that if we attempt the solution of the problem
, using the
method of separation of variables
then
where r is the separation variable. The function X(x, y) will then be required to
solve the eigenvalue problem
, and the eigenvalue will be precisely
the separation variable.
Higher spatial oscillations can be expected since a larger eigenvalue will generate
larger time variations that will have to be matched, through the original equation,
with higher spatial derivatives.
For the heat equation solved on arbitrary domains we will expect the eigenvalues
to be real and negative (decay). On the other hand, for the wave equation the
eigenvalues will be purely imaginary (propagation). Finally, we will expect the
eigenvalues to be general complex numbers when convective and diffusive terms
are present in the equation.
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REFERENCES
[F] S.J. Farlow, Partial Differential Equations for Scientists and Engineers,
Dover 1993.
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